With rise of smart medical sensors, cloud computing and the health care technologies, “connected health” is getting remarkable consideration everywhere. Recently, the Internet of Things (IoT) has brought the vision ...
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ISBN:
(数字)9783030979294
ISBN:
(纸本)9783030979287;9783030979317
With rise of smart medical sensors, cloud computing and the health care technologies, “connected health” is getting remarkable consideration everywhere. Recently, the Internet of Things (IoT) has brought the vision of a smarter world into reality. Cloud computing fits well in this scenario as it can provide high quality of clinical experience. Thus an IoT-cloud convergence can play a vital role in healthcare by offering better insight of heterogeneous healthcare content supporting quality care. It can also support powerful processing and storage facilities of huge data to provide automated decision making. This book aims to report quality research on recent advances towards IoT-Cloud convergence for smart healthcare, more specifically to the state-of-the-art approaches, design, development and innovative use of those convergence methods for providing insights into healthcare service demands. Students, researchers, and medical experts in the field of information technology, medicine, cloud computing, soft computing technologies, IoT and the related fields can benefit from this handbook in handling real-time challenges in healthcare. Current books are limited to focus either on soft computing algorithms or smart healthcare. Integration of smart and cloud computing models in healthcare resulting in connected health is explored in detail in this book.
Foundations of computationalintelligence Volume 5: Function Approximation and Classification Approximation theory is that area of analysis which is concerned with the ability to approximate functions by simpler and m...
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ISBN:
(数字)9783642015366
ISBN:
(纸本)9783642015359;9783642424397
Foundations of computationalintelligence Volume 5: Function Approximation and Classification Approximation theory is that area of analysis which is concerned with the ability to approximate functions by simpler and more easily calculated functions. It is an area which, like many other fields of analysis, has its primary roots in the mat- matics. The need for function approximation and classification arises in many branches of applied mathematics, computer science and data mining in particular. This edited volume comprises of 14 chapters, including several overview Ch- ters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of function approximation and classification. Besides research ar- cles and expository papers on theory and algorithms of function approximation and classification, papers on numerical experiments and real world applications were also encouraged. The Volume is divided into 2 parts: Part-I: Function Approximation and Classification – Theoretical Foundations Part-II: Function Approximation and Classification – Success Stories and Real World Applications Part I on Function Approximation and Classification – Theoretical Foundations contains six chapters that describe several approaches Feature Selection, the use Decomposition of Correlation Integral, Some Issues on Extensions of Information and Dynamic Information System and a Probabilistic Approach to the Evaluation and Combination of Preferences Chapter 1 “Feature Selection for Partial Least Square Based Dimension Red- tion” by Li and Zeng investigate a systematic feature reduction framework by combing dimension reduction with feature selection. To evaluate the proposed framework authors used four typical data sets.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications...
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ISBN:
(数字)9783031535031
ISBN:
(纸本)9783031535024;9783031535055
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2023). The carefully selected papers cover a wide range of theoretical topics such as network embedding and network geometry; community structure, network dynamics; diffusion, epidemics and spreading processes; machine learning and graph neural networks as well as all the main network applications, including social and political networks; networks in finance and economics; biological networks and technological networks.
This book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel searc...
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ISBN:
(数字)9783031892844
ISBN:
(纸本)9783031892837;9783031892868
This book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel search mechanisms, and the application of learning concepts to refine evolutionary operators. Furthermore, it emphasizes the significance of diversity and opposition in preventing premature convergence and improving algorithmic efficiency. These strategies collectively contribute to the development of more adaptive and robust optimization techniques. The book was designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in Science, Electrical Engineering, or computational Mathematics. Furthermore, engineering practitioners unfamiliar with metaheuristic computations will find value in the application of these techniques to address complex real-world engineering problems, extending beyond theoretical constructs.
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement...
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ISBN:
(数字)9788132215639
ISBN:
(纸本)9788132215622;9788132228967
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.
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